U.S. patent number 8,254,676 [Application Number 11/967,464] was granted by the patent office on 2012-08-28 for methods and systems for identifying a thin object.
This patent grant is currently assigned to Morpho Detection, Inc.. Invention is credited to Todd Gable, Dimitrios Ioannou.
United States Patent |
8,254,676 |
Ioannou , et al. |
August 28, 2012 |
Methods and systems for identifying a thin object
Abstract
A method for identifying an object within a container is
provided. The method includes acquiring image data representing an
image, applying a morphological operator to the acquired image data
to generate morphed image data, calculating a histogram based on
the morphed image data, and classifying the image using the
calculated histogram. A classification of the image may be
displayed and/or stored in a computer-readable memory.
Inventors: |
Ioannou; Dimitrios (Fremont,
CA), Gable; Todd (Newark, CA) |
Assignee: |
Morpho Detection, Inc. (Newark,
CA)
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Family
ID: |
40798532 |
Appl.
No.: |
11/967,464 |
Filed: |
December 31, 2007 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20090169104 A1 |
Jul 2, 2009 |
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Current U.S.
Class: |
382/170; 382/168;
382/308 |
Current CPC
Class: |
G06K
9/4647 (20130101); G06K 2209/09 (20130101) |
Current International
Class: |
G06K
9/00 (20060101); G06K 9/56 (20060101) |
Field of
Search: |
;382/181,170,168,173,190,191,232,254,276,305,224,308 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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WO0042566 |
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Jul 2000 |
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WO |
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WO0042567 |
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Jul 2000 |
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WO |
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Primary Examiner: Chawan; Sheela
Attorney, Agent or Firm: Armstong Teasdale LLP
Claims
What is claimed is:
1. A method for identifying an object within a container, said
method comprising: acquiring image data representing an image;
applying a morphological operator to the acquired image data to
generate morphed image data in which each image element of the
image data is assigned an integer value representing contact with
neighboring image elements; calculating, by a processor, a
histogram based on the integer values of each image element in the
morphed image data; classifying the image using the calculated
histogram; and outputting a classification of the image.
2. A method in accordance with claim 1, further comprising
segmenting the acquired image data into a plurality of image
segments.
3. A method in accordance with claim 2, further comprising labeling
each image segment of the plurality of image segments.
4. A method in accordance with claim 1, wherein applying a
morphological operator further comprises performing a binary
operation on the acquired image data to assign each image element a
binary value.
5. A method in accordance with claim 4, wherein applying a
morphological operator further comprises assigning the integer
value to each image element of the acquired image data based on a
binary value of an image element of interest and binary values of
image elements surrounding the image element of interest.
6. A method in accordance with claim 1, wherein applying a
morphological operator further comprises applying an averaging
filter to the acquired image data.
7. A method in accordance with claim 1, wherein classifying the
image using the calculated histogram further comprises comparing
the calculated histogram to a plurality of pre-stored image
signatures.
8. A method in accordance with claim 1, wherein classifying the
image further comprises classifying the image as one of a bulk
object, a thin object, and a random aggregation of image
elements.
9. A system for identifying an object within a container, said
system comprising: a data collection system; and a detection
classification system coupled to said data collection system, said
detection classification system configured to: acquire image data
representing an image; apply a morphological operator to the
acquired image data to generate morphed image data in which each
image element of the image data is assigned an integer value
representing contact with neighboring image elements; calculate a
histogram based on the integer values of each image element in the
morphed image data; classify the image using the calculated
histogram; and output a classification of the image.
10. A system in accordance with claim 9, wherein said detection
classification system is further configured to segment the acquired
image data into a plurality of image segments.
11. A system in accordance with claim 10, wherein said detection
classification system is further configured to label each image
segment of the plurality of image segments.
12. A system in accordance with claim 9, wherein said detection
classification system is further configured to perform a binary
operation on the acquired image data to assign each image element a
binary value.
13. A system in accordance with claim 12, wherein said detection
classification system is further configured to assign the integer
value to each image element of the acquired image data based on a
binary value of an image element of interest and binary values of
image elements surrounding the image element of interest.
14. A system in accordance with claim 9, wherein said detection
classification system is further configured to apply an averaging
filter to the acquired image data.
15. A system in accordance with claim 9, wherein said detection
classification system is further configured to compare the
calculated histogram to a plurality of pre-stored image
signatures.
16. A system in accordance with claim 9, wherein said detection
classification system is further configured to classify the image
as one of a bulk object, a thin object, and a random aggregation of
image elements.
17. A computer program embodied on a non-transitory
computer-readable medium, said computer program comprising a code
segment that configures a processor to: receive image data
representing an image; apply a morphological operator to the
acquired image data to generate morphed image data in which each
image element of the image data is assigned an integer value
representing contact with neighboring image elements; calculate a
histogram based on the integer values of each image element in the
morphed image data; classify the image using the calculated
histogram; and output a classification of the image.
18. A computer program embodied on a non-transitory
computer-readable medium in accordance with claim 17, wherein the
code segment further configures the processor to: perform a binary
operation on the acquired image data; apply an averaging filter to
the acquired image data; and assign the integer value to each image
element of the acquired data.
Description
FIELD OF THE INVENTION
The embodiments described herein relate generally to identifying a
shape of an object and, more particularly, to identifying the shape
of an object within a container to facilitate detecting contraband
concealed within the container.
BACKGROUND OF THE INVENTION
Known identification systems image a container to determine whether
explosives, drugs, weapons, and/or other contraband are present
within the container. Some of the known systems are configured to
determine whether a thin object is present within the container. At
least one known method for detecting objects in computed tomography
(CT) data, including sheet-shaped objects such as sheet explosives,
includes analyzing a neighborhood of voxels surrounding a test
voxel and eroding the data by identifying a neighborhood of voxels
surrounding a voxel of interest. In such a method, if the number of
voxels having densities below a predetermined threshold exceeds a
predetermined number, then it is assumed that the test voxel is a
surface voxel and is removed from the object. The known method also
includes applying a connectivity process to voxels to combine them
into objects after sheets are detected to prevent sheets from being
inadvertently removed from the data by erosion. Then a dilation
function can then be performed on the eroded object to replace
surface voxels removed by erosion. However, such known methods may
generate false alarms because random pixels are connected and are
then identified as a thin object, when no thin object exists.
Other known identification methods use density and/or atomic number
to identify components of an object, but are not specifically
directed to identifying a thin object.
BRIEF DESCRIPTION OF THE INVENTION
In one aspect, a method for identifying an object within a
container is provided. The method includes acquiring image data
representing an image, applying a morphological operator to the
acquired image data to generate morphed image data, calculating a
histogram based on the morphed image data, and classifying the
image using the calculated histogram. A classification of the image
is outputted.
In another aspect, a system for identifying an object within a
container is provided. The system includes a data collection system
and a detection classification system, wherein the detection
classification system is coupled to the data collection system. The
detection classification system is configured to acquire image data
representing an image, apply a morphological operator to the
acquired image data to generate morphed image data, calculate a
histogram based on the morphed image data, and classify the image
using the calculated histogram. A classification of the image is
outputted.
In still another aspect, a computer program embodied on a
computer-readable medium is provided. The computer program includes
a code segment that configures a processor to receive image data
representing an image, apply a morphological operator to the
acquired image data to generate morphed image data, calculate a
histogram based on the morphed image data, and classify the image
using the calculated histogram. A classification of the image is
outputted.
BRIEF DESCRIPTION OF THE DRAWINGS
FIGS. 1-4 show exemplary embodiments of the systems and methods
described herein. The embodiments shown in FIGS. 1-4 and described
by reference to FIGS. 1-4 are exemplary only.
FIG. 1 is a block diagram of an exemplary detection classification
system.
FIG. 2 is a flowchart of an exemplary embodiment of a method for
classifying an object that may be used with the system shown in
FIG. 1.
FIG. 3 is a flowchart of an exemplary embodiment of a morphological
operation that may be used with the method shown in FIG. 2.
FIG. 4 is a flowchart of an exemplary embodiment of a
classification operation that may be used with the method shown in
FIG. 2.
DETAILED DESCRIPTION OF THE INVENTION
The embodiments described herein provide systems and methods for
processing the output of an imaging system that includes a
detection and/or classification component, and for determining
whether a bulk object includes a thin object. In one embodiment, a
detection classification system receives images from an imaging
system. Using image elements making up the images, the detection
classification system classifies one or more segments of each
object as a thin object, a bulk object, or a sheet-like object. As
used herein, the term "thin object" may be used interchangeably
with "sheet" and refers to an object having opposing surfaces that
are separated by a relatively small thickness, especially by
comparison to the length and/or width of the object. Further, as
used herein, the term "sheet-like" may be used interchangeably with
"random object" and refers to a random aggregation of pixels and/or
voxels that appears to have the characteristics of a thin object,
but does not represent a physical object. Moreover, as used herein,
the term "bulk object" refers to an object having a distinct mass
or portion of matter, especially a large one, such that a bulk
object is a main or greater object within a container. A bulk
object does not have one dimension that is relatively much smaller
than other dimensions of the bulk object. Further, a bulk object
may represent more than one physical object. For example, a bulk
object may represent a plurality of sticks of an explosive
material.
For example, a book is included within a container, and the book
includes therein explosives configured to be inserted into the book
such that the explosives appear to be a page in the book. In the
example, the book is a bulk object, the explosives are a thin
object, and, if another object having sheet-like properties appears
to be imaged, those pixels and/or voxels form a sheet-like object.
The objects identified as thin objects may be further processed to
determine if explosives, drugs, weapons, and/or other contraband is
present within a container.
A technical effect of the systems and methods described herein is
to reduce the occurrence of false alarms by discriminating the
shape of a detected object and/or to recognize a thin object within
a container. An embodiment of a method uses a morphological
operator and a histogram-based descriptor to identify sheet-like
shapes and classify the shapes as thin objects or sheet-like
objects. Embodiments of the systems and methods described herein
may be used to avoid false alarms associated with sheet-like
shapes, such as random aggregations of voxels and/pixels, by
discriminating between thin objects and sheet-like objects.
At least one embodiment of the present invention is described below
in reference to its application in connection with and operation of
a system for inspecting cargo. However, it should be apparent to
those skilled in the art and guided by the teachings herein
provided that the invention is likewise applicable to any suitable
system for scanning cargo containers including, without limitation,
crates, boxes, drums, baggage, containers, luggage, and suitcases,
transported by water, land, and/or air, as well as other containers
and/or objects.
Moreover, although embodiments of the present invention are
described below in reference to its application in connection with
and operation of a system incorporating an X-ray computed
tomography (CT) scanning system for inspecting cargo, it should
apparent to those skilled in the art and guided by the teachings
herein provided that any suitable radiation source including,
without limitation, neutrons or gamma rays, may be used in
alternative embodiments. Further, it should be apparent to those
skilled in the art and guided by the teachings herein provided that
any scanning system may be used that produces a sufficient number
of pixels and/or voxels to enable the functionality of the
detection classification system described herein.
FIG. 1 is a block diagram of an exemplary detection classification
system 50 used with an X-ray computed tomography (CT) scanning
system 10 for scanning a container 12, such as a cargo container,
box, parcel, luggage, or suitcase, to identify the contents and/or
determine the type of material contained within container 12. The
term "contents" as used herein refers to any object and/or material
contained within container 12 and may include contraband.
In one embodiment, scanning system 10 includes at least one X-ray
source 14 configured to transmit at least one primary beam 15 of
radiation through container 12. In an alternative embodiment,
scanning system 10 includes a plurality of X-ray sources 14
configured to emit radiation of different energy distributions.
Alternatively, each X-ray source 14 is configured to emit radiation
of selective energy distributions, which can be emitted at
different times. In a particular embodiment, scanning system 10
utilizes multiple-energy scanning to obtain an attenuation map for
container 12. In addition to the production of CT images,
multiple-energy scanning enables the production of density maps and
atomic number of the object contents. In one embodiment, the dual
energy scanning of container 12 includes inspecting container 12 by
scanning container 12 at a low energy and then scanning container
12 at a high energy. The data is collected for the low-energy scan
and the high-energy scan to reconstruct the CT, density, and/or
atomic number images of container 12 to facilitate identifying the
type of material within container 12 based on the material content
of container 12 to facilitate detecting contraband concealed within
container 12, as described in greater detail below.
In one embodiment, scanning system 10 also includes at least one
X-ray detector 16 configured to detect radiation emitted from X-ray
source 14 and transmitted through container 12. X-ray detector 16
is configured to cover an entire field of view or only a portion of
the field of view. Upon detection of the transmitted radiation,
X-ray detector 16 generates a signal representative of the detected
transmitted radiation. The signal is transmitted to a data
collection system and/or processor as described below. Upon
detection of the transmitted radiation, each X-ray detector element
generates a signal representative of the detected transmitted
radiation. The signal is transmitted to a data collection system
and/or processor as described below. Scanning system 10 is utilized
to reconstruct a CT image of container 12 in real time, non-real
time, or delayed time.
In one embodiment of scanning system 10, a data collection system
18 is operatively coupled to and in signal communication with X-ray
detector 16. Data collection system 18 is configured to receive the
signals generated and transmitted by X-ray detector 16. A processor
20 is operatively coupled to data collection system 18. Processor
20 is configured to produce or generate one or more images of
container 12 and its contents and to process the produced image(s)
to facilitate determining the material content of container 12.
More specifically, in one embodiment, data collection system 18
and/or processor 20 produces at least one attenuation map based
upon the signals received from X-ray detector 16. Utilizing the
attenuation map(s), at least one image of the contents is
reconstructed and a CT number, a density, and/or an atomic number
of the contents is inferred from the reconstructed image(s). Based
on these CT images, density and/or atomic maps of container 12 can
be produced. The CT, density, and/or atomic number images are
analyzed to infer the presence of contraband, including, without
limitation, explosives and/or explosive materials.
In alternative embodiments of scanning system 10, one processor 20
or more than one processor 20 may be used to generate and/or
process the container image(s). In the exemplary embodiment,
scanning system 10 also includes a display device 22, a memory
device 24 and/or an input device 26 operatively coupled to data
collection system 18 and/or processor 20. As used herein, the term
"processor" is not limited to only integrated circuits referred to
in the art as a processor, but broadly refers to a computer, a
microcontroller, a microcomputer, a programmable logic controller,
an application specific integrated circuit and any other
programmable circuit. The processor 20 may also include a storage
device and/or an input device, such as a mouse and/or a
keyboard.
During operation of an embodiment of scanning system 10, X-ray
source 14 emits X-rays in an energy range, which is dependent on a
voltage applied by a power source to X-ray source 14. A primary
radiation beam 15 is generated and passes through container 12, and
X-ray detector 16, positioned on the opposing side of container 12,
measures an intensity of primary radiation beam 15.
Images generated by scanning system 10 are then processed by
detection classification system 50 to determine whether container
12 includes suspected contraband. More specifically, detection
classification system 50 uses the data within the images to
identify objects 28 and/or 30 within container 12 as a thin object,
a bulk object, or a sheet-like object. In the exemplary embodiment,
detection classification system 50 includes one or more processors
52 electrically coupled to a system bus (not shown). Detection
classification system 50 also includes a memory 54 electrically
coupled to the system bus such that memory 54 is communicatively
coupled to processor 52. Detection classification system 50 also
includes a display device 58, which may be, but is not limited to
being, a monitor (not shown), a cathode ray tube (CRT) (not shown),
a liquid crystal display (LCD) (not shown), and/or any other
suitable output device that enables system 50 to function as
described herein. Detection classification system 50 may also
include a storage device and/or an input device, such as a mouse
and/or a keyboard. In the exemplary embodiment, the results of
detection classification system 50 is output to a memory, such as
memory 54, a drive (not shown), a display device, such as display
device 58, and/or any other suitable component.
FIG. 2 shows a flowchart illustrating a method 100 for classifying
object 28 and/or 30 (shown in FIG. 1) as a sheet object using
detection classification system 50 (shown in FIG. 1). In the
exemplary embodiment, method 100 is implemented on system 10 and/or
system 50, however, method 100 is not limited to implementation on
system 10 and/or system 50, and rather, method 100 may be embodied
on a computer readable medium as a computer program, and/or
implemented and/or embodied by any other suitable means. The
computer program may include a code segment that, when executed by
a processor, configures the processor to perform one or more of the
function of method 100.
Furthermore, the results of method 100 are output 170 to a memory,
such as memory 54 (shown in FIG. 1), a drive (not shown), a display
device, such as display device 58 (shown in FIG. 1), and/or any
other suitable component. In one embodiment, a classification of
object 28 and/or 30 is output 170 such that the classification is
displayed to an operator and/or stored in computer-readable memory.
Although the method 100 is described as being used with a
three-dimensional image including voxels, the method 100 may also
be used with a two-dimensional image including pixels. As used
herein, the term "image element" refers to an element, such as a
pixel and/or voxel, within image data.
In the exemplary embodiment, detection classification system 50
(shown in FIG. 1) receives original image data I.sub.O acquired 110
by scanning system 10 (shown in FIG. 1). The original image data
I.sub.O represents an image of an object, such as container 12
(shown in FIG. 1), that has been scanned by scanning system 10.
Original image data I.sub.O is segmented 120 into a plurality of
image segments I.sub.S. After original data I.sub.O is segmented
120, each image segment I.sub.S is labeled 130. In the exemplary
embodiment, each image segment I.sub.S is labeled 130 as either a
bulk object image segment I.sub.B or a thin object image segment
I.sub.T. In another embodiment, at least the image segments I.sub.S
that may represent a thin object are labeled 130 and a thin object
image segment I.sub.T. In one embodiment, a plurality of bulk
object image segments I.sub.B1-I.sub.BN and a plurality of thin
object image segments I.sub.T1-I.sub.TN are labeled 130. Such
labeling facilitates identifying potentially suspect regions within
a container. In alternative embodiments, the original image data
I.sub.O is not segmented 120 and/or labeled 130, but rather, the
original image data I.sub.O is used for the following steps 140,
150, and 160.
After labeling 130, in the exemplary embodiment, a morphological
operator M(I.sub.S) is applied 140 to each thin object image
segment I.sub.T to generate a morphed image segment I.sub.M of the
object image segment I.sub.T. In an alternative embodiment, the
morphological operator M(I.sub.S) is applied 140 to bulk object
image segments I.sub.B and/or thin object image segments I.sub.T.
In the exemplary embodiment, the morphological operator M(I.sub.S)
is an averaging filter, as described in more detail herein. In an
alternative embodiment, the morphological operator M(I.sub.S) is
any suitable operator that enables method 100 to function as
described herein. The morphological operator M(I.sub.S), in the
exemplary embodiment, is applied 140 to each image element, such as
a pixel and/or a voxel, within the image data of the image segment
I.sub.S. In the exemplary embodiment, a histogram H(I.sub.M) is
then calculated 150 from the morphed image segment I.sub.M.
In the exemplary embodiment, memory 54 (shown in FIG. 1) includes
pre-stored segment signatures I.sub.SIG. More specifically, in the
exemplary embodiment, each segment signature I.sub.SIG represents a
histogram H.sub.B of a known bulk object, a histogram H.sub.T of a
known thin object, and/or a histogram H.sub.R of a known random
aggregation of voxels. The segment signatures I.sub.SIG are used by
a classification operator C to classify 160 the image segment
I.sub.S based on the calculated histogram H(I.sub.M), as described
in more detail herein. More specifically, classification operator C
classifies 160 each image segment Is as a bulk object O.sub.B, a
thin object O.sub.T, or a random aggregation of voxels O.sub.R. As
used herein, "random aggregation of voxels" and/or "random object"
refers to a segment of voxels within the acquired original image
data I.sub.O that appears to represent a physical object but no
such physical objects exists.
In one embodiment, an image segment I.sub.S that was labeled 130 as
a thin object image segment I.sub.T is classified as a thin object
O.sub.T or a random object O.sub.R by using method 100. Further, by
using method 100, an object 30 (shown in FIG. 1) is separated from
the contents within container 12 and is labeled 130 and classified
160 as a thin object O.sub.T that may be contraband, within
container 12. In one embodiment, the thin object O.sub.T is
processed further to determine whether the thin object O.sub.T is
an explosive material and/or a narcotic material. In an alternative
embodiment, steps 140, 150, and 160 are performed separately for
each labeled bulk object image segment I.sub.B1-I.sub.BN and/or
each labeled thin object image segment I.sub.T1-I.sub.TN in the
acquired original image data I.sub.O. More specifically, at least
each labeled thin object image segment I.sub.T1-I.sub.TN is
classified 160 as a bulk object O.sub.B, a thin object O.sub.T, or
a random object O.sub.R. In the exemplary embodiment, method 100
identifies object 30 (shown in FIG. 1) as a thin object O.sub.T and
object 28 (shown in FIG. 1) as a bulk object O.sub.B, for
example.
FIG. 3 is a flowchart of an exemplary embodiment of the application
140 of the morphological operator M(I.sub.S) in method 100.
In the exemplary embodiment, the labeled thin object image segments
I.sub.T and/or the labeled bulk object image segments I.sub.B are
received 142 by the morphological operator M(I.sub.S). More
specifically, each image segment I.sub.Slabeled as a thin object
image segment I.sub.T is received 142 by the morphological operator
M(I.sub.S). A binary operation B(I.sub.S) is performed 144 on the
image segment I.sub.S to generate a binary image I.sub.BIN of the
image segment. In the exemplary embodiment, the binary segment
I.sub.BIN is convolved 146 using a volumetric operator, such as,
for example, a three-by-three-by-three operator, wherein all
coefficients are equal to one. As such, each voxel of the image
segment I.sub.S is assigned 148 an integer value based on how many
other voxels are in contact with and/or surrounded by the voxel of
interest. In the exemplary embodiment, each voxel is assigned 148 a
value between "0" and "27," and all subranges therebetween, wherein
"0" indicates that no other voxels are in contact with the voxel of
interest and "27" indicates that the voxel of interest is
completely surrounded by other voxels. Accordingly, a bulk object
will include more voxels having higher values, a thin object will
include more voxels having mid-range values, and a random object
will include more voxels having lower values. In the exemplary
embodiment, the values assigned 148 to the voxels are used to
calculate 150 the histogram H(I.sub.M) of the image segment
I.sub.S, wherein the histogram H(I.sub.M) includes an observed
frequency for each value from "0" to "27", and all subranges
therebetween.
FIG. 4 is a flowchart of an exemplary embodiment of classification
160 by the classification operator C in method 100.
The histogram H(I.sub.M) is received 162 by the classification
operator C. In the exemplary embodiment, the classification
operator C accesses 164 the pre-stored segment signatures
I.sub.SIG, and compares 166 the received histogram H(I.sub.M) to
the segment signatures I.sub.SIG. More specifically, in the
exemplary embodiment, the received histogram H(I.sub.M) is compared
to the segment signatures I.sub.SIG to determine to which segment
signature I.sub.SIG the histogram H(I.sub.M) is most closely
analogous. In one embodiment, if the histogram H(I.sub.M) has a
high frequency for the value "27," for example, the histogram
H(I.sub.M) is most analogous to the segment signature I.sub.SIG of
a bulk object, if the histogram H(I.sub.M) has a high frequency for
the values between "11" and "13," for example, the histogram
H(I.sub.M) is most analogous to the segment signature I.sub.SIG of
a thin object, and if the histogram H(I.sub.M) has a high frequency
for the values between "2" and "7," for example, the histogram
H(I.sub.M) is most analogous to the segment signature I.sub.SIG of
a random object or sheet-like object. Alternatively, any other
suitable range and/or subrange of values is used to determine an
analogous segment signature I.sub.SIG. Based on comparison 166
between the histogram H(I.sub.M) and the pre-stored segment
signatures I.sub.SIG, the classification operator C classifies 168
the image segment I.sub.S, represented by the histogram H(I.sub.M)
as a bulk object O.sub.B, a thin object O.sub.T, or a random object
O.sub.R.
The above-described systems and methods for identifying a thin
object facilitate improving the reliability of detecting a thin
object by reducing the number of false alarms. More specifically,
because the objects within a container are classified as a thin
object, a bulk object, or a random object, the systems and methods
identify fewer non-existent thin objects as compared to known thin
object identification methods and/or systems. Further, the methods
described herein enable further processing of a detected thin
object to determine whether the thin object is contraband. The
above-described methods provide generic, robust methods for thin
structure classification for passenger luggage and/or other
inspection systems. As such, the methods use a number of robust
features to discriminate among thin structures, bulk structures,
and sheet-like structures.
Exemplary embodiments of methods and systems for identifying a thin
object are described above in detail. The methods and systems are
not limited to the specific embodiments described herein, but
rather, components of systems and/or steps of the methods may be
utilized independently and separately from other components and/or
steps described herein. For example, the methods may also be used
in combination with other imaging systems and methods, and are not
limited to practice with only the classification systems as
described herein. Rather, the exemplary embodiment can be
implemented and utilized in connection with many other
identification and/or classification applications.
Although specific features of various embodiments of the invention
may be shown in some drawings and not in others, this is for
convenience only. In accordance with the principles of the
invention, any feature of a drawing may be referenced and/or
claimed in combination with any feature of any other drawing.
While the methods and systems described herein have been described
in terms of various specific embodiments, those skilled in the art
will recognize that the methods and systems described herein can be
practiced with modification within the spirit and scope of the
appended claims.
* * * * *